ArticleGesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany))2023
[The Diagnostic Test: Goodness, Characteristics, and Interpretation: Under the Impact of the Corona Pandemic and Different SARS-CoV-2 Tests].
Article in Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 1 citations in OpenAlex.
- A unified framework for diagnostic test development and evaluation during outbreaks of emerging infections.Communications medicine · 2024Review
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMany diagnostic tests are currently being performed around the world to detect SARS-CoV-2 infection. Positive and negative test results are not one hundred percent accurate, but have far-reaching consequences. There are false positives (test positive, uninfected) and false negatives (test negative, infected). A positive/negative result does not necessarily mean that the test subject is actually infected/non-infected. This article has two objectives: 1. to explain the most important characteristics of diagnostic tests with binary outcome 2. to point out problems and phenomena of interpretation of diagnostic tests, on the basis of different scenarios.
methodPresentation of the basic concepts of the quality of a diagnostic test (sensitivity, specificity) and pre-test probability (prevalence of test group). Calculation (including formulas) of further important quantities.
resultsIn the basic scenario, sensitivity is 100%, specificity 98.8%, and pre-test probability of 1.0% (10 infected persons per 1,000 tested). For 1,000 diagnostic tests, the statistical mean is 22 positive cases, 10 of which are true-positive. The positive predictive probability is 45.7%. The prevalence calculated from this (22/1,000 tests) overestimates the actual prevalence (10/1,000 tests) by a factor of 2.2. All cases with a negative test outcome are true negative. The prevalence has a strong influence on the positive and negative predictive value. This phenomenon occurs even with otherwise very good test values of sensitivity and specificity. At a prevalence of only 5 infected persons per 10,000 (0.05%), the positive predictive probability drops to 4.0%. Lower specificity amplifies this effect, especially with small numbers of infected persons.
conclusionIf the sensitivity or specificity is below 100%, diagnostic tests are always error-prone. If the prevalence of infected persons is low, a large number of false positive results are to be expected - even if the test is of good quality with a high sensitivity and especially a high specificity. This is accompanied by low positive predictive values, i. e. positive tested persons are not infected. A false positive test result in the first test can be clarified by carrying out a second test.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.